Senior Engineer, EMS & AI Automation

Parallel Wireless is a U.S.-based pioneer in Open RAN innovation, transforming how mobile networks are built, optimized, and powered. Through our GreenRAN™ portfolio, we enable operators to deliver next-generation connectivity with unmatched energy efficiency, automation, and flexibility.

What you need - 8+ years of professional software development experience, with 3+ years of production Go (Golang)

Strong grasp of Go idioms: goroutines/channels, context propagation, interfaces and composition, error handling

Solid experience with MongoDB (or comparable NoSQL): schema design, indexing, aggregation pipelines, replica sets/sharding

Hands-on experience with Kafka or similar messaging systems in event-driven architectures

Experience designing and building RESTful APIs in distributed microservice systems

Strong understanding of concurrency, high availability, and multi-replica deployment concerns (shared state, locking, idempotency)

Working knowledge of AI-assisted development tools across the SDLC — using AI coding assistants and agents (e.g., for code generation, refactoring, test writing, debugging, and code review) to improve development speed and quality, with sound judgment on validating and reviewing AI-generated output.

Preferred / Nice to Have

Telecom domain experience: RAN, EMS/NMS, O-RAN architecture (Near-RT RIC, SMO), 4G/5G

Familiarity with device management protocols: TR-069 (CWMP), NETCONF/YANG, SNMP, O1

Exposure to 3GPP or O-RAN specifications and OAM/FCAPS concepts

What you will do - Design, develop, and maintain Go microservices for RAN device configuration, fault management, and network optimization

Build and evolve protocol adapters that translate between internal data models and device-facing protocols

Design MongoDB schemas, DAOs, and efficient queries (projections, indexes, aggregations, change streams) for a sharded production cluster

Develop event-driven flows using Kafka for faults, cell-state changes, and configuration propagation across services

Ensure services are HA-ready: multi-replica safe, stateless where possible, with graceful shutdown and proper concurrency control

Write meaningful unit tests, participate in code reviews, and uphold security, performance, and error-handling standards

Debug production issues across services using logs, metrics, and distributed tracing of data flows

Leverage AI tools throughout the SDLC — from design and coding to testing, documentation, and troubleshooting — to accelerate delivery while ensuring output is reviewed, validated, and production-ready.